Metals profile of milled shrimps and the potential risk associated with their consumption
Bibliographic record
Abstract
Abstract The presence of metals in milled shrimps sold on some major markets in Kumasi were investigated to ascertain their levels and the potential health risk they may pose to humans when ingested, due to the level of pollution in the marine environment where these shrimps are obtained from. The samples, which comprised of 30 composites, were analysed using x-ray florescence spectrometry and found to contain Co, Cr, Cu, Fe, K, Mo, Ca, Zn, As, Sr, and Zr with average concentrations of 4.09 mg kg− 1, 5.17 mg kg− 1, 25.14 mg kg− 1, 351.47 mg kg− 1, 9050.74 mg kg− 1, 4.08 mg kg− 1, 21984.48 mg kg− 1, 696.89 mg kg− 1, 8.99 mg kg− 1, 328.54 mg kg− 1, and 9.86 mg kg− 1 respectively. Non-carcinogenic risk indicators analysed suggested a likelihood of health hazard when the milled shrimps are ingested, particularly concerning is the levels of arsenic determined. The arsenic may, however, be in organic form which will make it less of a concern. The levels of the metals could not be linked statistically to the milling process after comparing them to procured controls, which may suggest that these metals may have been picked up in the aquatic environment and/or prior to milling. There is a need, therefore, for action to reduce pollution and remediate the aquatic environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".